The document presents a methodology using Bayesian estimation to predict wave-induced flooding in reef environments, addressing the challenges posed by varying offshore conditions and coral morphology. It details simulations conducted with the XBeach non-hydrostatic model and validates the predictions with limited field data. The findings suggest that the model can assist in early-warning systems and climate change impact assessments, highlighting the importance of certain parameters like offshore wave conditions and reef width for flood hazard estimation.

1. 1
BEWARE: Bayesian estimation of
wave attack in reef environments
Stuart Pearson1,2, Ap van Dongeren1, Curt Storlazzi3,
Marion Tissier2, Ad Reniers2, John Phillip Lapidez4,
Yoshimitsu Tajima4, Takenori Shimozono4

2. Background
2
 Reef-fronted tropical coastlines are faced with an
increasing threat of wave-induced flooding
 However, this flooding is challenging to predict due to:
 Variations in offshore hydrodynamic forcing
 Vast range of coral reef morphologies
 Complexity of coral reef hydrodynamics
(USGS, 2014)

3. Methodology: XBeach Non-Hydrostatic
3
 Limited field data available!
 Need to generate synthetic dataset
 Used XBeach Non-Hydrostatic model
 Validated for reefs using lab data of Demirbilek et al (2007)
 Varied reef morphology and hydrodynamic forcing
 Carried out ~174,000 simulations (7 params, 3-7 variations)
Idealized Reef
Profile

4. Methodology: Bayesian Networks Example
4
 What is the likelihood of flooding on an island 2 m
above sea level?
 Prior prediction (no additional information):
 Updated (Posterior) Prediction (with additional information):
High Tide
Hs=3.0 m
Tp=18 s
Wreef = 150 m
Cf = 0.01
Reef Slope = 1/2
Beach Slope = 1/10
Runup > 2 m
(83% chance)
All Possible
Hydrodynamic
Conditions
All Reefs in
Database
Runup > 2 m
(40% chance)
Prunup
(%)
Runup
Prunup
(%)
Runup

5.  Probabilistic graphical model
 Visually represents conditional probabilities through a
series of nodes and connections
 “Trained” using real or synthetic dataset
 Fast and proven in other coastal contexts
Methodology: Bayesian Network
5
𝐻 𝑉𝐿𝐹
Hydrodynamic
Forcing
Reef
Morphology
Hazard Outputs
𝜂0
𝜂
𝛽𝑓
𝛽 𝑏
𝑐𝑓
𝑊𝑟𝑒𝑒𝑓
𝐻0
𝐻0 𝐿0
𝐻 𝑆𝑆
𝐻IG
𝑅2
%
𝑇 𝑚−1,0

6. Results: XBeach Non-Hydrostatic
6
 Runup trends reflect field and laboratory observations
(a) (b) (c) (d)
(e) (f) (g)

7. Results: Bayesian Network Validation
7
 Limited cases available
 BN can predict majority
of tested cases
 R2% predictions vary
 SS, LF predictions good
 Setup overestimated
 Need more field data!

8. Ongoing: Application to Typhoon Meranti in Philippines
8
 Ran 86 392 new cases in XBNH based on Typhoon Meranti
 Compared with runup measurements from Tajima et al. (2017)
 Tendency to overestimate runup
 Due to simplifications made in schematizing the model?
 1D XBeach model overestimates IG component
 We do not account for directional effects or refraction/diffraction
Batanes

9. Results: Bayesian Network Predictive Skill
9
 How well can the network predict cases it has not
seen before?
 Performed k-fold validation on XBNH dataset
 Good predictions of high runup events
 Less predictive skill for VLF waves  resonance?

10. Results: Log-Likelihood Ratios
10
 Which variables are most important for predictions?
 Withhold each input variable one-at-a-time from the BN
More complex
process?
Less
important
More important
Prediction
using
entire
network

11. Conclusions
11
 XBNH can reproduce wave transformation processes on
fringing reefs, including resonant reef flat amplification
 BEWARE shows high predictive skill for flooding
conditions from the XBNH model
 Validated for a limited number of case studies
 Offshore wave conditions, water level, and reef width are
the most important parameters to estimate flood hazards
 Having knowledge of the reef roughness or beach slope
appears less important
 BEWARE can form the basis for early-warning systems
and scenario assessment applications on reef-lined coasts
 e.g. SLR, wave climate, reef restoration scenarios
 Couple with 2D inundation models and damage estimators

12. Recommendations
12
 Collect more validation data
 Small-Scale
 Field measurements of hydrodynamics (especially runup)
 e.g. Estimates of storm impacts and flooding
 Large-Scale
 Need more data on reef morphology & offshore forcing
 e.g. Remote sensing of reef flats, offshore waves
 Use BEWARE as a tool in real-life cases
 Early warning systems
 Climate change impact assessments
(USGS, 2016)

13. Further Information
13
 BEWARE Database:
 Available online soon (or by request: s.g.pearson@tudelft.nl)
 Related Publications:
 Pearson, S. G.; C.D. Storlazzi; A.R. van Dongeren; M.F.S. Tissier; and
A.J.H.M. Reniers. 2017. “A Bayesian-Based System to Assess Wave-
Driven Flooding Hazards on Coral Reef-Lined Coasts.” Journal of
Geophysical Research: Oceans (In Press).
 Tajima, Y.; J.P. Lapidez; J. Camelo; M. Saito; Y. Matsuba; T. Shimozono;
D. Bautista; M. Turiano; and E Cruz. 2017. “Post-Disaster Survey of
Storm Surge and Waves Along the Coast of Batanes, the
Philippines, Caused by Super Typhoon Meranti/Ferdie.” Coastal
Engineering Journal 59 (1): 1750009.
Thank you for your time!